Controlling operation of a machine by performing reconciliation using a distributed cluster of nodes

ABSTRACT

The operation of a machine can be controlled by performing reconciliation using a cluster of nodes. In one example, a node can receive parent timestamped data from a parent dataset and child timestamped data from child datasets that are children of the parent dataset in a hierarchical relationship. The parent timestamped data and the child timestamped data can relate to an operational characteristic of the machine. The node can generate computer processing-threads. Each computer processing-thread can solve one or more respective reconciliation problems between a parent data point that has a particular timestamp in the parent timestamped data and child data points that also have the particular timestamp in the child timestamp data to generate a reconciled dataset. An operational setting of the machine can then be adjusted based on the reconciled dataset.

REFERENCE TO RELATED APPLICATION

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/454,787, filed Feb. 4, 2017, theentirety of which is hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to multicomputer datatransferring and distributed data processing. More specifically, but notby way of limitation, this disclosure relates to controlling operationof a machine by performing reconciliation using a distributed cluster ofnodes.

BACKGROUND

Machines can perform a variety of tasks. Examples of machines caninclude rubber presses, hydrocarbon extraction tools, actuators, robots,vehicles, or any combination of these. Some machines can be controlledby a computer, which can analyze data and control the machine based onresults from the analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 is a block diagram of an example of the hardware components of acomputing system according to some aspects.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system including a variety of control and worker nodesaccording to some aspects.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system including a control node and a worker node according tosome aspects.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or processing project according to some aspects.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects.

FIG. 10 is a block diagram of an ESP system interfacing between apublishing device and multiple event subscribing devices according tosome aspects.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects.

FIG. 12 is an example of a neural network according to some aspects.

FIG. 13 is a block diagram of an example of a distributed computingenvironment according to some aspects.

FIG. 14 is a block diagram of a hierarchy of data according to someaspects.

FIG. 15 is a block diagram of reconciliation occurring in a distributedcomputing environment according to some aspects.

FIG. 16 is a flow chart of an example of a process for controllingoperation of a machine by performing reconciliation using a distributedcluster of nodes according to some aspects.

FIG. 17 is a table of examples of the amount of time it takes one ormore nodes to perform a reconciliation process according to someaspects.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The figures and description are notintended to be restrictive.

The ensuing description provides examples only, and is not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the ensuing description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the following description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples can be described as a process that is depictedas a flowchart, a flow diagram, a data flow diagram, a structurediagram, or a block diagram. Although a flowchart can describe theoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. A process is terminated when itsoperations are completed, but can have additional operations notincluded in a figure. A process can correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

Systems depicted in some of the figures can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

FIGS. 1-12 depict examples of systems and methods usable for controllingoperation of a machine by performing reconciliation using a distributedcluster of nodes according to some aspects. For example, FIG. 1 is ablock diagram of an example of the hardware components of a computingsystem according to some aspects. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. The computing environment 114 may include one or more othersystems. For example, computing environment 114 may include a databasesystem 118 or a communications grid 120. The computing environment 114can include one or more processing devices (e.g., distributed over oneor more networks or otherwise in communication with one another) that,in some examples, can collectively be referred to as a processor or aprocessing device.

Data transmission network 100 also includes one or more network devices102. Network devices 102 may include client devices that can communicatewith computing environment 114. For example, network devices 102 maysend data to the computing environment 114 to be processed, may sendcommunications to the computing environment 114 to control differentaspects of the computing environment or the data it is processing, amongother reasons. Network devices 102 may interact with the computingenvironment 114 through a number of ways, such as, for example, over oneor more networks 108.

In some examples, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP)), to the computing environment 114 vianetworks 108. For example, the network devices 102 can transmitelectronic messages for controlling operation of a machine or performingreconciliation, all at once or streaming over a period of time, to thecomputing environment 114 via networks 108.

The network devices 102 may include network computers, sensors,databases, or other devices that may transmit or otherwise provide datato computing environment 114. For example, network devices 102 mayinclude local area network devices, such as routers, hubs, switches, orother computer networking devices. These devices may provide a varietyof stored or generated data, such as network data or data specific tothe network devices 102 themselves. Network devices 102 may also includesensors that monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devices102 may provide data they collect over time. Network devices 102 mayalso include devices within the internet of things, such as deviceswithin a home automation network. Some of these devices may be referredto as edge devices, and may involve edge-computing circuitry. Data maybe transmitted by network devices 102 directly to computing environment114 or to network-attached data stores, such as network-attached datastores 110 for storage so that the data may be retrieved later by thecomputing environment 114 or other portions of data transmission network100. For example, the network devices 102 can transmit data usable forcontrolling operation of a machine or performing reconciliation to anetwork-attached data store 110 for storage. The computing environment114 may later retrieve the data from the network-attached data store 110and use the data to control operation of a machine or performreconciliation.

Network-attached data stores 110 can store data to be processed by thecomputing environment 114 as well as any intermediate or final datagenerated by the computing system in non-volatile memory. But in certainexamples, the configuration of the computing environment 114 allows itsoperations to be performed such that intermediate and final data resultscan be stored solely in volatile memory (e.g., RAM), without arequirement that intermediate or final data results be stored tonon-volatile types of memory (e.g., disk). This can be useful in certainsituations, such as when the computing environment 114 receives ad hocqueries from a user and when responses, which are generated byprocessing large amounts of data, need to be generated dynamically(e.g., on the fly). In this situation, the computing environment 114 maybe configured to retain the processed information within memory so thatresponses can be generated for the user at different levels of detail aswell as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data stores may includestorage other than primary storage located within computing environment114 that is directly accessible by processors located therein.Network-attached data stores may include secondary, tertiary orauxiliary storage, such as large hard drives, servers, virtual memory,among other types. Storage devices may include portable or non-portablestorage devices, optical storage devices, and various other mediumscapable of storing, containing data. A machine-readable storage mediumor computer-readable storage medium may include a non-transitory mediumin which data can be stored and that does not include carrier waves ortransitory electronic communications. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode or machine-executable instructions that may represent a procedure,a function, a subprogram, a program, a routine, a subroutine, a module,a software package, a class, or any combination of instructions, datastructures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, among others. Furthermore,the data stores may hold a variety of different types of data. Forexample, network-attached data stores 110 may hold unstructured (e.g.,raw) data.

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time-stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data or structured hierarchically according to oneor more dimensions (e.g., parameters, attributes, or variables). Forexample, data may be stored in a hierarchical data structure, such as arelational online analytical processing (ROLAP) or multidimensionalonline analytical processing (MOLAP) database, or may be stored inanother tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the server farms 106 or one or more servers within the server farms106. Server farms 106 can be configured to provide information in apredetermined manner. For example, server farms 106 may access data totransmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or morewebsites, sensors, as inputs from a control database, or may have beenreceived as inputs from an external system or device. Server farms 106may assist in processing the data by turning raw data into processeddata based on one or more rules implemented by the server farms. Forexample, sensor data may be analyzed to determine changes in anenvironment over time or in real-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain examples, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork 116 can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, orsystems. In some examples, the computers, servers, or systems that makeup the cloud network 116 are different from the user's own on-premisescomputers, servers, or systems. For example, the cloud network 116 mayhost an application, and a user may, via a communication network such asthe Internet, order and use the application on demand. In some examples,the cloud network 116 may host an application for controlling operationof a machine or performing reconciliation.

While each device, server, and system in FIG. 1 is shown as a singledevice, multiple devices may instead be used. For example, a set ofnetwork devices can be used to transmit various communications from asingle user, or remote server 140 may include a server stack. As anotherexample, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between server farms 106 and computing environment 114, or between aserver and a device) may occur over one or more networks 108. Networks108 may include one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108. The networks 108 can be incorporatedentirely within or can include an intranet, an extranet, or acombination thereof. In one example, communications between two or moresystems or devices can be achieved by a secure communications protocol,such as secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The computing nodes in the communicationsgrid 120 and the transmission network database system 118 may share thesame processor hardware, such as processors that are located withincomputing environment 114.

In some examples, the computing environment 114, a network device 102,or both can implement one or more processes for controlling operation ofa machine or performing reconciliation. For example, the computingenvironment 114, a network device 102, or both can implement one or moreversions of the processes discussed with respect to any of the figures.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects. Asnoted, each communication within data transmission network 100 may occurover one or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). In someexamples, the communication can include times series data. Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. In someexamples, the network device 204 may collect data either from itssurrounding environment or from other network devices (such as networkdevices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems. The network devices may detectand record data related to the environment that it monitors, andtransmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data itcollects before transmitting the data to the computing environment 214,or before deciding whether to transmit data to the computing environment214. For example, network devices 204-209 may determine whether datacollected meets certain rules, for example by comparing data or valuescalculated from the data and comparing that data to one or morethresholds. The network devices 204-209 may use this data or comparisonsto determine if the data is to be transmitted to the computingenvironment 214 for further use or processing. In some examples, thenetwork devices 204-209 can pre-process the data prior to transmittingthe data to the computing environment 214. For example, the networkdevices 204-209 can reformat the data before transmitting the data tothe computing environment 214 for further processing (e.g., analyzingthe data to control operation of a machine or perform reconciliation).

Computing environment 214 may include machines 220, 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines 220,240, computing environment 214 may have only one machine or may havemore than two machines. The machines 220, 240 that make up computingenvironment 214 may include specialized computers, servers, or othermachines that are configured to individually or collectively processlarge amounts of data. The computing environment 214 may also includestorage devices that include one or more databases of structured data,such as data organized in one or more hierarchies, or unstructured data.The databases may communicate with the processing devices withincomputing environment 214 to distribute data to them. Since networkdevices may transmit data to computing environment 214, that data may bereceived by the computing environment 214 and subsequently stored withinthose storage devices. Data used by computing environment 214 may alsobe stored in data stores 235, which may also be a part of or connectedto computing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze or store data from or pertaining tocommunications, client device operations, client rules, oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include amachine 240 that is a web server. Computing environment 214 can retrievedata of interest, such as client information (e.g., product information,client rules, etc.), technical product details, news, blog posts,e-mails, forum posts, electronic documents, social media posts (e.g.,Twitter™ posts or Facebook™ posts), time series data, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices 204-209 may receive data periodically and in real time from aweb server or other source. Devices within computing environment 214 mayalso perform pre-analysis on data it receives to determine if the datareceived should be processed as part of an ongoing project. For example,as part of a project in which a machine is controlled from data, thecomputing environment 214 can perform a pre-analysis of the data. Thepre-analysis can include determining whether the data is in a correctformat for controlling operation of a machine or performingreconciliation using the data and, if not, reformatting the data intothe correct format.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects. More specifically, FIG. 3identifies operation of a computing environment in an Open SystemsInteraction model that corresponds to various connection components. Themodel 300 shows, for example, how a computing environment, such ascomputing environment (or computing environment 214 in FIG. 2) maycommunicate with other devices in its network, and control howcommunications between the computing environment and other devices areexecuted and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 arearranged in a stack. Each layer in the stack serves the layer one levelhigher than it (except for the application layer, which is the highestlayer), and is served by the layer one level below it (except for thephysical layer 302, which is the lowest layer). The physical layer 302is the lowest layer because it receives and transmits raw bites of data,and is the farthest layer from the user in a communications system. Onthe other hand, the application layer is the highest layer because itinteracts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer302 represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagneticcommunications. Physical layer 302 also defines protocols that maycontrol communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid-computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network.In other words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid-computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availability orcommunication content or formatting using the applications.

For example, a communication link can be established between two deviceson a network. One device can transmit an analog or digitalrepresentation of an electronic message that includes a dataset to theother device. The other device can receive the analog or digitalrepresentation at the physical layer 302. The other device can transmitthe data associated with the electronic message through the remaininglayers 304-314. The application layer 314 can receive data associatedwith the electronic message. The application layer 314 can identify oneor more applications, such as an application for controlling operationof a machine or performing reconciliation, to which to transmit dataassociated with the electronic message. The application layer 314 cantransmit the data to the identified application.

Intra-network connection components 322, 324 can operate in lowerlevels, such as physical layer 302 and link layer 304, respectively. Forexample, a hub can operate in the physical layer, a switch can operatein the physical layer, and a router can operate in the network layer.Inter-network connection components 326, 328 are shown to operate onhigher levels, such as layers 306-314. For example, routers can operatein the network layer and network devices can operate in the transport,session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in variousexamples, one, more, all or any of the various layers. For example,computing environment 330 can interact with a hub (e.g., via the linklayer) to adjust which devices the hub communicates with. The physicallayer 302 may be served by the link layer 304, so it may implement suchdata from the link layer 304. For example, the computing environment 330may control which devices from which it can receive data. For example,if the computing environment 330 knows that a certain network device hasturned off, broken, or otherwise become unavailable or unreliable, thecomputing environment 330 may instruct the hub to prevent any data frombeing transmitted to the computing environment 330 from that networkdevice. Such a process may be beneficial to avoid receiving data that isinaccurate or that has been influenced by an uncontrolled environment.As another example, computing environment 330 can communicate with abridge, switch, router or gateway and influence which device within thesystem (e.g., system 200) the component selects as a destination. Insome examples, computing environment 330 can interact with variouslayers by exchanging communications with equipment operating on aparticular layer by routing or modifying existing communications. Inanother example, such as in a grid-computing environment, a node maydetermine how data within the environment should be routed (e.g., whichnode should receive certain data) based on certain parameters orinformation provided by other layers within the model.

The computing environment 330 may be a part of a communications gridenvironment, the communications of which may be implemented as shown inthe protocol of FIG. 3. For example, referring back to FIG. 2, one ormore of machines 220 and 240 may be part of a communicationsgrid-computing environment. A gridded computing environment may beemployed in a distributed system with non-interactive workloads wheredata resides in memory on the machines, or compute nodes. In such anenvironment, analytic code, instead of a database management system, cancontrol the processing performed by the nodes. Data is co-located bypre-distributing it to the grid nodes, and the analytic code on eachnode loads the local data into memory. Each node may be assigned aparticular task, such as a portion of a processing project, or toorganize or control other nodes within the grid. For example, each nodemay be assigned a portion of a processing task for controlling operationof a machine or performing reconciliation.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system 400 including a variety of control and worker nodesaccording to some aspects. Communications grid computing system 400includes three control nodes and one or more worker nodes.Communications grid computing system 400 includes control nodes 402,404, and 406. The control nodes are communicatively connected viacommunication paths 451, 453, and 455. The control nodes 402-406 maytransmit information (e.g., related to the communications grid ornotifications) to and receive information from each other. Althoughcommunications grid computing system 400 is shown in FIG. 4 as includingthree control nodes, the communications grid may include more or lessthan three control nodes.

Communications grid computing system 400 (which can be referred to as a“communications grid”) also includes one or more worker nodes. Shown inFIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six workernodes, a communications grid can include more or less than six workernodes. The number of worker nodes included in a communications grid maybe dependent upon how large the project or dataset is being processed bythe communications grid, the capacity of each worker node, the timedesignated for the communications grid to complete the project, amongothers. Each worker node within the communications grid computing system400 may be connected (wired or wirelessly, and directly or indirectly)to control nodes 402-406. Each worker node may receive information fromthe control nodes (e.g., an instruction to perform work on a project)and may transmit information to the control nodes (e.g., a result fromwork performed on a project). Furthermore, worker nodes may communicatewith each other directly or indirectly. For example, worker nodes maytransmit data between each other related to a job being performed or anindividual task within a job being performed by that worker node. Insome examples, worker nodes may not be connected (communicatively orotherwise) to certain other worker nodes. For example, a worker node 410may only be able to communicate with a particular control node 402. Theworker node 410 may be unable to communicate with other worker nodes412-420 in the communications grid, even if the other worker nodes412-420 are controlled by the same control node 402.

A control node 402-406 may connect with an external device with whichthe control node 402-406 may communicate (e.g., a communications griduser, such as a server or computer, may connect to a controller of thegrid). For example, a server or computer may connect to control nodes402-406 and may transmit a project or job to the node, such as a projector job related to controlling operation of a machine or performingreconciliation. The project may include the dataset. The dataset may beof any size and can include a time series. Once the control node 402-406receives such a project including a large dataset, the control node maydistribute the dataset or projects related to the dataset to beperformed by worker nodes. Alternatively, for a project including alarge dataset, the dataset may be receive or stored by a machine otherthan a control node 402-406 (e.g., a Hadoop data node).

Control nodes 402-406 can maintain knowledge of the status of the nodesin the grid (e.g., grid status information), accept work requests fromclients, subdivide the work across worker nodes, and coordinate theworker nodes, among other responsibilities. Worker nodes 412-420 mayaccept work requests from a control node 402-406 and provide the controlnode with results of the work performed by the worker node. A grid maybe started from a single node (e.g., a machine, computer, server, etc.).This first node may be assigned or may start as the primary control node402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (e.g., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node 402 receivesa project, the primary control node may distribute portions of theproject to its worker nodes for execution. For example, a project forcontrolling operation of a machine or performing reconciliation can beinitiated on communications grid computing system 400. A primary controlnode can control the work to be performed for the project in order tocomplete the project as requested or instructed. The primary controlnode may distribute work to the worker nodes 412-420 based on variousfactors, such as which subsets or portions of projects may be completedmost efficiently and in the correct amount of time. For example, aworker node 412 may perform reconciliation using at least a portion ofdata that is already local (e.g., stored on) the worker node. Theprimary control node also coordinates and processes the results of thework performed by each worker node 412-420 after each worker node412-420 executes and completes its job. For example, the primary controlnode may receive a result from one or more worker nodes 412-420, and theprimary control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404, 406, may beassigned as backup control nodes for the project. In an example, backupcontrol nodes may not control any portion of the project. Instead,backup control nodes may serve as a backup for the primary control nodeand take over as primary control node if the primary control node wereto fail. If a communications grid were to include only a single controlnode 402, and the control node 402 were to fail (e.g., the control nodeis shut off or breaks) then the communications grid as a whole may failand any project or job being run on the communications grid may fail andmay not complete. While the project may be run again, such a failure maycause a delay (severe delay in some cases, such as overnight delay) incompletion of the project. Therefore, a grid with multiple control nodes402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listeningsockets to add another node or machine to the grid. A socket may be usedto accept work requests from clients, and the second socket may be usedto accept connections from other grid nodes. The primary control nodemay be provided with a list of other nodes (e.g., other machines,computers, servers, etc.) that can participate in the grid, and the rolethat each node can fill in the grid. Upon startup of the primary controlnode (e.g., the first node on the grid), the primary control node mayuse a network protocol to start the server process on every other nodein the grid. Command line parameters, for example, may inform each nodeof one or more pieces of information, such as: the role that the nodewill have in the grid, the host name of the primary control node, theport number on which the primary control node is accepting connectionsfrom peer nodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, recoveredfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it can check to see if it alreadyhas a connection to that other node. If it does not have a connection tothat node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. But, a hierarchy of nodes may also be determinedusing methods other than using the unique identifiers of the nodes. Forexample, the hierarchy may be predetermined, or may be assigned based onother predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404, 406 (and, for example, toother control or worker nodes 412-420 within the communications grid).Such communications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes 410-420 in the communications grid, unique identifiers of theworker nodes 410-420, or their relationships with the primary controlnode 402) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes 410-420 inthe communications grid. The backup control nodes 404, 406 may receiveand store the backup data received from the primary control node 402.The backup control nodes 404, 406 may transmit a request for such asnapshot (or other information) from the primary control node 402, orthe primary control node 402 may send such information periodically tothe backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 totake over as primary control node if the primary control node 402 failswithout requiring the communications grid to start the project over fromscratch. If the primary control node 402 fails, the backup control node404, 406 that will take over as primary control node may retrieve themost recent version of the snapshot received from the primary controlnode 402 and use the snapshot to continue the project from the stage ofthe project indicated by the backup data. This may prevent failure ofthe project as a whole.

A backup control node 404, 406 may use various methods to determine thatthe primary control node 402 has failed. In one example of such amethod, the primary control node 402 may transmit (e.g., periodically) acommunication to the backup control node 404, 406 that indicates thatthe primary control node 402 is working and has not failed, such as aheartbeat communication. The backup control node 404, 406 may determinethat the primary control node 402 has failed if the backup control nodehas not received a heartbeat communication for a certain predeterminedperiod of time. Alternatively, a backup control node 404, 406 may alsoreceive a communication from the primary control node 402 itself (beforeit failed) or from a worker node 410-420 that the primary control node402 has failed, for example because the primary control node 402 hasfailed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404,406) can take over for failed primary control node 402 and become thenew primary control node. For example, the new primary control node maybe chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative example, a backupcontrol node may be assigned to be the new primary control node byanother device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeexample, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeexample, the primary control node may transmit a communication to eachof the operable worker nodes still on the communications grid that eachof the worker nodes should purposefully fail also. After each of theworker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed. In someexamples, a communications grid computing system 400 can be used tocontrol operation of a machine or perform reconciliation.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects. The process may include,for example, receiving grid status information including a projectstatus of a portion of a project being executed by a node in thecommunications grid, as described in operation 502. For example, acontrol node (e.g., a backup control node connected to a primary controlnode and a worker node on a communications grid) may receive grid statusinformation, where the grid status information includes a project statusof the primary control node or a project status of the worker node. Theproject status of the primary control node and the project status of theworker node may include a status of one or more portions of a projectbeing executed by the primary and worker nodes in the communicationsgrid. The process may also include storing the grid status information,as described in operation 504. For example, a control node (e.g., abackup control node) may store the received grid status informationlocally within the control node. Alternatively, the grid statusinformation may be sent to another device for storage where the controlnode may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system 600 including a control node and a worker nodeaccording to some aspects. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viacommunication path 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain examples, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or a processing project according to some aspects. As describedwith respect to FIG. 6, the GESC at the control node may transmit datawith a client device (e.g., client device 630) to receive queries forexecuting a project and to respond to those queries after large amountsof data have been processed. The query may be transmitted to the controlnode, where the query may include a request for executing a project, asdescribed in operation 702. The query can contain instructions on thetype of data analysis to be performed in the project and whether theproject should be executed using the grid-based computing environment,as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects. ESPE 800 may includeone or more projects 802. A project may be described as a second-levelcontainer in an engine model managed by ESPE 800 where a thread poolsize for the project may be defined by a user. Each project of the oneor more projects 802 may include one or more continuous queries 804 thatcontain data flows, which are data transformations of incoming eventstreams. The one or more continuous queries 804 may include one or moresource windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeexample, there may be only one ESPE 800 for each instance of the ESPapplication, and ESPE 800 may have a unique engine name. Additionally,the one or more projects 802 may each have unique project names, andeach query may have a unique continuous query name and begin with auniquely named source window of the one or more source windows 806. ESPE800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects. As noted, the ESPE 800 (or an associated ESP application)defines how input event streams are transformed into meaningful outputevent streams. More specifically, the ESP application may define howinput event streams from publishers (e.g., network devices providingsensed data) are transformed into meaningful output event streamsconsumed by subscribers (e.g., a data analytics project being executedby a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. Variousoperations may be performed in parallel, for example, using a pluralityof threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing betweenpublishing device 1022 and event subscribing devices 1024 a-c accordingto some aspects. ESP system 1000 may include ESP device or subsystem1001, publishing device 1022, an event subscribing device A 1024 a, anevent subscribing device B 1024 b, and an event subscribing device C1024 c. Input event streams are output to ESP device 1001 by publishingdevice 1022. In alternative embodiments, the input event streams may becreated by a plurality of publishing devices. The plurality ofpublishing devices further may publish event streams to other ESPdevices. The one or more continuous queries instantiated by ESPE 800 mayanalyze and process the input event streams to form output event streamsoutput to event subscribing device A 1024 a, event subscribing device B1024 b, and event subscribing device C 1024 c. ESP system 1000 mayinclude a greater or a fewer number of event subscribing devices ofevent subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of thepublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on publishing device 1022.The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analyticsproject after the data is received and stored. In other examples,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the present disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations, suchas those in support of an ongoing manufacturing or drilling operation.An example of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, one ormore processors and one or more computer-readable mediums operablycoupled to the one or more processor. The processor is configured toexecute an ESP engine (ESPE). The computer-readable medium hasinstructions stored thereon that, when executed by the processor, causethe computing device to support the failover. An event block object isreceived from the ESPE that includes a unique identifier. A first statusof the computing device as active or standby is determined. When thefirst status is active, a second status of the computing device as newlyactive or not newly active is determined. Newly active is determinedwhen the computing device is switched from a standby status to an activestatus. When the second status is newly active, a last published eventblock object identifier that uniquely identifies a last published eventblock object is determined. A next event block object is selected from anon-transitory computer-readable medium accessible by the computingdevice. The next event block object has an event block object identifierthat is greater than the determined last published event block objectidentifier. The selected next event block object is published to anout-messaging network device. When the second status of the computingdevice is not newly active, the received event block object is publishedto the out-messaging network device. When the first status of thecomputing device is standby, the received event block object is storedin the non-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12. The neural network 1200 is represented asmultiple layers of interconnected neurons, such as neuron 1208, that canexchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 and adesired output of the neural network 1200. Based on the gradient, one ormore numeric weights of the neural network 1200 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neuralnetwork 1200. For example, this process can be repeated hundreds orthousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:

y=max(x,0)

where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1204, of the neural network 1200. The subsequent layerof the neural network 1200 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1200. This process continues until the neural network 1200outputs a final result at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedilyexecuted and processed with machine-learning specific processors (e.g.,not a generic CPU). For example, some of these processors can include agraphical processing unit (GPU), an application-specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), a TensorProcessing Unit (TPU) by Google, and/or some other machine-learningspecific processor that implements one or more neural networks usingsemiconductor (e.g., silicon (Si) or gallium arsenide(GaAs)) devices.

Certain aspects and features of the present disclosure relate tocontrolling operation of a machine by performing reconciliation using adistributed cluster of nodes. Reconciliation can include adjusting dataat one or more levels in a hierarchy of data to conform the hierarchy ofdata to one or more constraints. As a particular example, a node in thedistributed cluster of nodes can receive data points having values witha particular timestamp (e.g., the same timestamp). An example of theparticular timestamp may be Nov. 16, 2017, at 9:27 AM. The data pointscan include a parent data point from a parent dataset and child datapoints from multiple child datasets, where the multiple child datasetsare children of the parent dataset in a hierarchical relationship. Thenode can generate a computer processing-thread and use the computerprocessing-thread to solve a reconciliation problem between the parentdata point and its children data points for the particular timestamp.Solving the reconciliation problem can include adjusting a value of achild data point subject to a constraint, for example, that an aggregatevalue of the adjusted versions of the child data points are to remainwithin a tolerance range (e.g., 10% or 0.1) of a value of the parentdata point. The node may repeat the above process for each timestamp inthe parent dataset, using a separate computer processing-thread to solveeach respective reconciliation problem for each respective timestamp.After performing some or all of the reconciliation processes todetermine the adjusted versions of the child data points, the node cananalyze the adjusted versions of the child data points to determineinformation relevant to controlling operation of the machine. The nodecan then use the information to control operation of the machine (e.g.,to modify one or more parameters of the machine).

As a specific example, a node can be used to analyze a hierarchy ofdata, where the hierarchy of data includes a hierarchy of predictiveforecasts related to how a machine will operate at a future point intime. After performing one or more reconciliation processes usingparallel processing, the adjusted hierarchy of predictive forecasts maybe more accurate. The node can then use the adjusted hierarchy ofpredictive forecasts to predict if an anomaly related to the machinewill occur at a future point in time. If so, the node may modify aparameter of the machine to reduce a likelihood of the anomaly occurringor alert an operator of the machine to enable the operator to takepreventative action.

Some examples of the present disclosure can operate a machine faster andmore precisely than alternative techniques. For example, to accuratelycontrol a machine, a large amount of data (e.g., thousands or hundredsof thousands of data points) may need to be analyzed. The large amountof data may relate to past performance of the machine, anomaliesassociated with the machine, or other characteristics of the machine.And analyzing a large amount of data using a single computer can be timeconsuming, computing-resource intensive (e.g., processing power, memory,or hard-disk space intensive), and inaccurate. But some examples of thepresent disclosure can overcome these and other issues by distributingthe analysis of a large amount of data among a cluster of nodes in adistributed computing environment. This can enable large amounts of datarelevant to controlling the machine to be analyzed in a small amount oftime (e.g., roughly 7 seconds). For example, this may enable a largeamount of real-time streaming data that is related to the machine to bereceived and analyzed in a small amount of time, so that the machine canbe controlled more quickly, accurately, and responsively. Othertechniques may take an unreasonable amount of time to process largeamounts of data, especially when the large amounts of data are receivedat extremely fast speeds (e.g., substantially real-time speeds).

Some examples of the present disclose can improve machines andmachine-based technologies by enabling the status of a machine to beanalyzed or predicted faster (e.g., in substantially real time),anomalies to be detected, and corrective action to be taken, ifnecessary. As a particular example, a robot may stream data to thedistributed cluster of nodes from a variety of sensors. The data fromthe sensors may have a hierarchical relationship and thereby form ahierarchy of data. Each node in the cluster can then analyze itsrespective portion of the data by processing each timestamp in itsrespective portion of the data using an independent computerprocessing-thread. Processing the data in this manner can result insignificantly faster processing speeds than processing the data usingalternative approaches. After processing the data, the distributedcluster of nodes can use the results to control the robot. For example,the distributed cluster of nodes can use the results to predict if therobot is going to collide (e.g., drive into) a physical object anddirect the robot to avoid the collision. As another example, thedistributed cluster of nodes can predict if the robot is going tomalfunction and change an operational setting of the robot to avoid themalfunction. Thus, the distributed cluster of nodes can act as a “brain”for the robot, processing sensor data at high speeds and providingsubstantially real-time feedback to the robot.

FIG. 13 is a block diagram of an example of a distributed computingenvironment 1302 according to some aspects. The distributed computingenvironment 1302 can include a cluster (e.g., group) of nodes 1304 a-nin communication with each other over a network for processing data in adistributed manner. The distributed computing environment 1302 caninclude any number and combination of nodes 1304-an. Examples of thenodes 1304 a-n can include a computing device, a server, a virtualmachine, or any combination of these.

Each of the nodes 1304 a-n can retrieve and process at least a portionof a dataset. For example, the nodes 1304 a-n can each retrieve arespective portion of the dataset from a database 1306 (or from anothernode), which can be internal or external to the distributed computingenvironment 1302. The nodes 1304 a-n can then analyze the respectiveportions of the dataset to determine information related to the dataset.

The distributed computing environment 1302 can be in communication withany number and combination of machines, such as machine 1308, which canbe internal or external to the distributed computing environment 1302.Examples of the machine can include a motor; a press; a power system; adigging or extraction tool, such as for earth, hydrocarbons, orminerals; a robot; a vehicle; a computing device or server; or anycombination of these. The machine may be able to execute one or morephysical processes.

The machine 1308 or another device may communicate information to thedistributed computing environment 1302 or store information in thedatabase 1306. The nodes 1304 a-n can access or receive the information,analyze the information, and control operation of the machine 1308 basedon the results of the analysis.

In some examples, the distributed computing environment 1302 canimplement a distributed reconciliation process to determine informationusable for controlling the machine 1308. A distributed reconciliationprocess can be a reconciliation process that is performed in adistributed manner by nodes 1304 a-n of a distributed computingenvironment 1302. A reconciliation process can be a process forreconciling hierarchical data, as is described in greater detail below.

Hierarchical data can include information that is related in ahierarchy. One example of such a hierarchy is shown in FIG. 14. In thishierarchy, circle 1-1 can be referred to as a parent and the remainingcircles can be referred to as children of circle 1-1. And circles 2-1and 2-2 can be referred to as direct children of circle 1-1, whereas theremaining circles can be referred to as indirect children of circle 1-1.Likewise, circle 2-1 can be a parent of circles 3-1 and 3-2. And circles3-1 and 3-2 can be direct children of circle 2-1, whereas circles 4-1,4-2, 4-3, and 4-4 can be indirect children of circle 2-1. And so on.Hierarchical data can be organized into any number and combination ofhierarchical levels.

A particular example of hierarchical data can include data packetsreceived by a server over a network. Circle 1-1 can represent a totalnumber of data packets received by the server over the network. Circle2-1 can represent the total number of data packets received by theserver from inside the United States. Circle 2-2 can represent the totalnumber of data packets received by the server from outside the UnitedStates. Circles 3-1 and 3-2 can represent the total numbers of datapackets received by the server from different geographical regionswithin the United States. Circles 3-3 and 3-4 can represent the totalnumbers of data packets received by the server from differentgeographical regions outside the United States. And so on. All of thevalues in the children circles may add up to the total number of datapackets received by the server in circle 1-1.

One type of hierarchical data is time series data. Time series data caninclude a series of data points arranged in a sequential order over theperiod of time. The data points can be equally spaced apart over theperiod of time (e.g., the data points can be obtained at a fixedfrequency). Magnitudes of the data points may provide information aboutan object over the period of time.

One example of time series data can include a predictive forecast. Thepredictive forecast can indicate demand for (e.g., sales of or interestin) an object over a future period of time. In such an example, circle1-1 can represent a predictive forecast indicating total demand for anobject over a future period of time. Circle 2-1 can represent apredictive forecast indicating the total demand for the object over afirst portion of the future period of time. Circle 2-2 can represent apredictive forecast indicating the total demand for the object over asecond portion of the future period of time. Circles 3-1 and 3-2 canrepresent predictive forecasts indicating the total demand for theobject from different sub-portions of the first portion of the futureperiod of time. Circles 3-3 and 3-4 can represent predictive forecastsindicating the total demand for the object from different sub-portionsof the second portion of the future period of time. And so on.

Some hierarchical data can conform to constraints that link togetherdifferent levels of the hierarchy. For example, hierarchical data canconform to an aggregation constraint or summation constraint, whereby aparent value in the hierarchical data is a sum of all the child valuesin the hierarchy. Other hierarchical data may not conform to one or moreconstraints. For example, time series data can be used to create apredictive forecast indicating total computer connections to a hostserver over a future period of time. This can be referred to as a totalpredictive forecast, and can be represented by circle 1-1 in FIG. 14.And portions of the time series data can be used to create predictiveforecasts indicating computer connections from different geographicalregions. These can be referred to as regional predictive forecasts,which can be children of the total predictive forecast and can berepresented by circles 2-1 and 2-2 in FIG. 14. The time series data canalso be used to create state-level predictive forecasts, which can bechildren of a regional predictive forecast and can be represented bycircles 3-1, 3-2, 3-3, and 3-4 in FIG. 14. And while the state-levelpredictive forecasts, regional predictive forecasts, and totalpredictive forecasts may form a hierarchy, they may not conform to oneor more constraints, such as an aggregation constraint. For example, thestate-level predictive forecasts may not aggregate to a regionalpredicative forecast for various reasons, such as the state-levelpredictive forecasts being created using a different methodology thanthe regional predictive forecasts. And the regional predictive forecastsmay not aggregate to the total predictive forecast for various reasons.In such situations, it may be desirable to adjust the hierarchical datato cause the hierarchical data to conform one or more constraints. Thiscan be achieved through a reconciliation process.

A reconciliation process (or simply “reconciliation”) can includetransforming (e.g., adjusting or modifying) data at the different levelsin a hierarchy to conform the hierarchy to one or more constraints. Forexample, the reconciliation process can include transforming values ofthe regional predictive forecasts to cause the regional predictiveforecasts to aggregate to the total predictive forecast. Thereconciliation process can additionally or alternatively includetransforming values of the state-level predicative forecasts to causethe state-level predictive forecasts to aggregate to a regionalpredictive forecast. The reconciliation process can include transformingany number and combination of values at any number and combinationlevels in a hierarchy to cause the hierarchy to conform to one or moreconstraints.

In some examples, the reconciliation process is performed by multiplenodes in a distributed manner. An example of such a distributedreconciliation process is shown in FIG. 15. The example shown in FIG. 15includes a parent time series and N child time-series, which arechildren of the parent time series. The parent time series and the Nchild time-series can collectively form hierarchical data. The parenttime series has multiple data points at different timestamps (e.g., t₀through t_(n)) during a time period. The N child time-series also havemultiple data points at the same timestamps (e.g., t₀ through t_(n))during the same time period as the parent time series. But the parenttime series and some or all of the N child time-series may not conformto a particular constraint, such as an aggregation constraint. Thus, itmay be desirable to reconcile some or all of the N child time-serieswith the parent time series. This reconciliation can be performed in adistributed manner using nodes 1302 a-n of the distributed computingenvironment 1302.

In some examples, each computer processing-thread on a node can performa reconciliation process for a particular timestamp. For example, node1302 a can receive a parent data point for timestamp t₁ (e.g., P_t₁ fromthe parent time series). Node 1302 a can also receive child data pointsfor timestamp t₁ (e.g., C1_t ₁ through CN_t₁). The node 1302 a mayreceive some or all of these from a database in response to a command,such as a read command, transmitted by the node 1302 a. The node 1302 acan then use a computer processing-thread (e.g., Computer processingthread 1 in FIG. 15) to handle the reconciliation process related totimestamp t₁. Similarly, node 1302 a can receive a parent data point fortimestamp t₂ and child data points for timestamp t₂. The node 1302 a canthen use Computer processing thread 2 to handle the reconciliationprocess related to timestamp t₂. In the example shown in FIG. 15, thisprocess is also repeated for timestamps t₃, t₄, and t₅. Also, other datafrom the parent time series and the children time series can beprocessed using another node, such as node 1302 n. In the example shownin FIG. 15, timestamps t_(n-2) through t_(n) are assigned to node 1302 nand simultaneously processed in parallel using Computer processingthreads 1-3. This distribution of data is for illustrative purposes, andin other examples the parent data points and child data points can bedistributed among any number and combination of nodes, so long as all ofthe data points for a particular timestamp are distributed to the samenode (and processed using the same computer processing-thread on thenode).

After a node receives the parent data point and child data points for aparticular timestamp, the node can perform the reconciliation processusing a computer processing-thread. For example, node 1302 a cangenerate (e.g., create, configure, and use) computer processing-threads1-5. And each computer processing-thread can be used to solve anindividual reconciliation problem between a parent data point and itschildren data points for a particular timestamp. For example, Computerprocessing thread 1 of node 1302 a can solve a reconciliation problembetween P_t₁, C1_t ₁, . . . , CN_t₁. The computer processing thread cansolve the reconciliation problem by transforming, for example, a valueof C1_t ₁. And computer processing thread 2 of node 1302 a can solve areconciliation problem between P_t₂, C1_t ₂, . . . , CN_t₂ bytransforming the values of any number and combination of the child datapoints. In some examples, transforming the values of the child datapoints can include adjusting the values of the child data points suchthat an aggregate value of the adjusted versions of the child datapoints is within a predefined tolerance range of the parent data point.For example, the value of C1_t ₁ can be adjusted to be closer to thevalue of P_t₁, while keeping the aggregate value of the adjustedversions of the child data points within the tolerance range of P_t₁ (orequal to P_t₁). As specific examples, the values of C1_t ₁ and CN_t₁ canbe adjusted such that an aggregate value of the adjusted versions of thechild data points is equal to P_t₁, within 0.1 of P_t₁, or within 5% ofP_t₁. The adjusted versions of the child data points can be referred toas reconciled child data-points.

After a node creates reconciled child data-points for a particulartimestamp, the node can write the reconciled child data-points to adatabase, transmit the reconciled data points, or otherwise use thereconciled data points. For example, node 1302 a can store thereconciled child data-points for t₁ in the database 1306, and node 1302n can store the reconciled child data-points for t_(n) in the database1306. In some examples, the distributed computing environment 1302(e.g., a node) can combine together the reconciled child data-pointsfrom multiple nodes 1302 a-n to form a combined reconciled dataset. Thecombined reconciled dataset may form a time series, such as a predictiveforecast. The distributed computing environment 1302 may then store thecombined reconciled dataset in a database for later retrieval.

Although FIG. 15 only shows one level of a hierarchical dataset—a singleparent time series and N children time series—there can be multiplelevels in a hierarchy, such as in the hierarchy shown in FIG. 14. Toaccommodate a hierarchical dataset with multiple levels, some examplesof the present disclosure can implement the distribution reconciliationprocess recursively, such as from the bottom of the hierarchy upwards orthe top of the hierarchy downwards. In a bottom-up example, the valuesin FIG. 14 represented by circles 4-1 through 4-8 can be adjusted toconform to a constraint relative to the values represented by circles3-1 through 3-4. Then, the values represented by circles 3-1 through 3-4can be adjusted to conform to a constraint relative to the valuesrepresented by circles 2-1 and 2-2. Then, the values represented bycircles 2-1 and 2-2 can be adjusted to conform to a constraint relativeto the value represented by circle 1-1. A top-down example can beimplemented in an opposite manner to the bottom-up example.

FIG. 15 shows a particular distributed reconciliation process forillustrative purposes. But other examples can perform a distributedreconciliation process in a different manner. Some examples can use anynumber and combination of nodes 1302 a-n, each executing any number andcombination of computer processing-threads, to implement a distributedreconciliation process.

In some examples, results of the distributed reconciliation process canbe used to control operation of a machine. FIG. 16 is a flow chart of anexample of a process for controlling operation of a machine byperforming reconciliation using a distributed cluster of nodes accordingto some aspects. Other examples can use more steps, fewer steps,different steps, or a different combination of the steps shown in FIG.16. The steps are described below with reference to the features ofFIGS. 13-15.

In block 1602, a node receives parent timestamped data from a parentdataset and child timestamped data from multiple child datasets. Thechild datasets can be children of the parent dataset in a hierarchicalrelationship. For example, parent dataset can include some or all ofP_t₁ through P_t_(n) in FIG. 15. And the child datasets can include C1_t₁ through C1_t _(n) from the CHILD_1, and CN_t₁ through CN_t_(n) fromCHILD_N, in FIG. 13. The node can receive the parent timestamped dataand/or the child timestamped data from a database, from another nodewithin a distributed computing environment 1302, from a computing deviceexternal to the distributed computing environment 1302, or from amachine 1308. In some examples, the parent timestamped data and thechild timestamped data relate to an operational characteristic of themachine (e.g., how the machine will operate at a future point in time).

In block 1604, the node generates computer processing-threads based onthe timestamps in the parent timestamped data. For example, the node candetermine that there are N timestamps (or N data points) in the parenttimestamped data and generate N computer processing-threads. The numberof computer processing-threads may or may not be equal to the number oftimestamps in the parent timestamped data. Each computerprocessing-thread can then be assigned to solve one or more respectivereconciliation problems involving a particular timestamp in the parenttimestamped data. For example, each computer processing-thread can solvea reconciliation problem between a particular parent data-point that hasa particular timestamp and one or more child data points that also havethe particular timestamp.

In block 1606, the node reconciles differences between the parent datapoint and the child data points assigned to each computerprocessing-thread. This can generate a reconciled dataset. Thereconciled dataset can be adjusted versions of the child data points.The node can reconcile the differences between the parent data point andthe child data points by using each respective computerprocessing-thread to adjust the respective values of one or more childdata points that are assigned to the respective computerprocessing-thread. In some examples, the node can adjust the respectivevalues of the child data points in a manner that maintains an aggregatevalue of the adjusted versions of the child data points within atolerance range of a value of the parent data point. For example, thenode can adjust a value for a first child-data point from 4.0 to 6.0, avalue of a second child data-point from 1.5 to 3.2, and a value of athird child data-point from 5.0 to 0.8, thereby maintaining theaggregate value of the adjusted versions of the child data points at10.0, which can be equal to (or within a predefined tolerance range of)the value of the parent data point.

In some examples, the node can reconcile the differences by adjustingthe child data points to minimize a loss function, such as:

$\sum\limits_{\{{i = 1}\}}^{m_{t}}\left\{ {\left( {{\overset{\sim}{x}}_{it} - {\hat{x}}_{it}} \right)^{2}*{\hat{x}}_{it}^{- \delta}{\hat{\sigma}}_{t}^{{- 2}\lambda}} \right\}$

where {circumflex over (x)}_(i) is a pre-reconciliation value for achild data point at time i; {tilde over (x)}_(i) is a reconciled valuefor the child data point at time i; {circumflex over (σ)} can be avector of standard errors for {circumflex over (x)}_(i); δ can bebetween 0 and 0.5; and λ can be between 0 and 1. The abovementioned lossfunction can be subject to a constraint, such as the following top-downconstraint:

${\sum\limits_{i}{\overset{\sim}{x}}_{i}} = \hat{y}$

where ŷ is a pre-reconciliation value of the parent data point. In someexamples, the value of the parent data point can be adjusted during areconciliation process additionally or alternatively to a child datapoint.

In block 1608, the node writes the values of the reconciled dataset to adatabase. For example, the node can directly write the values of thereconciled dataset to database 1306, or transmit the values of thereconciled dataset to another computing device, which in turn can writethe values of the reconciled dataset to the database 1306.

In block 1610, the node adjusts an operational setting of a machine 1308based on the reconciled dataset. In one example, the machine 1308 can bemonitored using the reconciled dataset to detect an event associatedwith the machine 1308. For example, the reconciled dataset can be usedto create a predictive forecast. The predicative forecast can indicatehow the machine 1308 will operate over a future period of time, ananomaly associated with the machine 1308, or another event associatedwith the machine 1308. The node can analyze the predictive forecast toidentify the event (e.g., anomaly) associated with the machine. Forexample, the node can receive sensor information from sensors coupled tothe machine 1308. The node can then compare the sensor information tovalues in the predictive forecast to determine if there arediscrepancies between the two. If so, the node can determine that ananomaly or other event associated with the machine 1308 may be occurringor may be likely to occur in the future. This can enable the maintenanceor another remedial procedure to be preemptively performed.

In some examples, the node performs a task or operates the machine inresponse to the event. For example, the node can create a predictiveforecast (e.g., as described in block 1610) and use the predictiveforecast to determine that the machine will be operating at anundesirable temperature or with an undesirable amount of pressure at afuture point in time. The node can then modify one or more parameters ofthe machine to reduce a likelihood of the machine operating at theundesirable temperature or with the undesirable amount of pressure atthe future point in time. As another example, the node may detect theevent and transmit a notification indicative of the event (e.g., toanother node or computing device). For example, the node may detect thatthe machine 1308 is about to fail and transmit a notification to anoperator of the machine 1308 to enable an operator of the machine 1308to perform preventative maintenance.

Some examples of the present disclosure can operate a machine faster andmore precisely than alternative techniques by distributing the analysisof a large amount of data related to the machine among a cluster ofnodes in a distributed computing environment. For example, as shown inFIG. 17, it may take a single node roughly 24 minutes of centralprocessing unit (CPU) time to perform a reconciliation process. But ifthe reconciliation process is distributed among four nodes, it can becompleted in roughly 1 minute and 24 seconds of CPU time. And if thereconciliation process is distributed among 141 nodes, it can becompleted in roughly 7.71 seconds of CPU time. The examples shown inFIG. 17 were implemented using 32 computer processing-threads per node,and may represent average values obtained after several runs (e.g.,because CPU speed can vary slightly due to several factors). As shown,by breaking a reconciliation problem down into smaller problems anddistributing the smaller problems among a cluster of nodes, improvedcomputational efficiency can be realized, enabling a machine to becontrolled more quickly and responsively than the machine may otherwisebe able to be controlled.

The foregoing description of certain examples, including illustratedexamples, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure.

1. A method for controlling operation of a machine using a cluster ofnodes in a distributed computing environment, the method comprising:receiving, by a node in the cluster of nodes, parent timestamped datafrom a parent dataset and child timestamped data from a plurality ofchild datasets that are children of the parent dataset in a hierarchicalrelationship, wherein the parent timestamped data and the childtimestamped data relate to an operational characteristic of the machine;generating, by the node, a plurality of computer processing-threads on asingle processing device, each computer processing-thread of theplurality of computer processing-threads being assigned to solve one ormore respective reconciliation problems between a parent data point thathas a particular timestamp in the parent timestamped data and child datapoints that also have the particular timestamp in the child timestampdata; reconciling, by the node, differences between the parent datapoint and the child data points assigned to each computerprocessing-thread using the plurality of computer processing-threads togenerate a reconciled dataset, wherein reconciling the differencescomprises each respective computer processing-thread of the plurality ofcomputer processing-threads adjusting values of the child data pointsthat are assigned to the respective computer processing-thread such thatan aggregate value of the adjusted versions of the child data points iswithin a tolerance range of a value of the parent data point; andadjusting, by the node, an operational setting of the machine based onthe reconciled dataset.
 2. The method of claim 1, wherein reconcilingthe differences between the parent data point and the child data pointscomprises adjusting at least one value of at least one child data pointto be closer to the value of the parent data point while maintaining theaggregate value of the adjusted versions of the child data points withinthe tolerance range.
 3. The method of claim 1, wherein the plurality ofcomputer processing-threads reconcile the differences between the parentdata point and the child data points in parallel to one another.
 4. Themethod of claim 1, wherein each node in the cluster of nodes has atleast one respective processing device with multiple computerprocessing-threads, and wherein each computer processing-thread amongthe multiple computer-processing threads solves a respectivereconciliation problem related to a unique timestamp in the parentdataset by reconciling differences between (i) a particular parentdata-point having the unique timestamp in the parent dataset and (ii)multiple child data-points having the unique timestamp in the pluralityof child datasets.
 5. The method of claim 1, further comprising:receiving the parent timestamped data and the child timestamped datafrom a remote database in response to a read command transmitted by thenode, the read command being one of a plurality of read commandsexecuted in parallel by the cluster of nodes in the distributedcomputing environment; and writing the reconciled dataset to the remotedatabase for later retrieval.
 6. The method of claim 1, wherein theparent dataset and the plurality of child datasets are predictiveforecasts projecting how the machine will operate at a future point intime.
 7. The method of claim 6, further comprising: generating apredictive forecast using reconciled dataset; and using the predictiveforecast to determine how the machine will operate at the future pointin time.
 8. The method of claim 7, further comprising: monitoring amachine using the predictive forecast to identify an event associatedwith the machine; and adjusting a parameter of the machine based on theevent.
 9. The method of claim 7, further comprising: detecting ananomaly with a machine using the predictive forecast; and based ondetecting the anomaly, performing a task configured to reduce alikelihood of the anomaly occurring.
 10. The method of claim 1, furthercomprising: receiving reconciled datasets from other nodes in thecluster of nodes, each reconciled dataset corresponding to differenttimestamps in the parent dataset; combining the reconciled datasets toform a combined reconciled dataset; and storing the combined reconcileddataset in a database for later retrieval.
 11. A node for a distributedcomputing environment, the node comprising: a processing device; and amemory device including instructions executable by the processing devicefor causing the processing device to: receive parent timestamped datafrom a parent dataset and child timestamped data from a plurality ofchild datasets that are children of the parent dataset in a hierarchicalrelationship, wherein the parent timestamped data and the childtimestamped data relate to an operational characteristic of a machine;generate a plurality of computer processing-threads on a singleprocessing device, each computer processing-thread of the plurality ofcomputer processing-threads being assigned to solve one or morerespective reconciliation problems between a parent data point that hasa particular timestamp in the parent timestamped data and child datapoints that also have the particular timestamp in the child timestampdata; reconcile differences between the parent data point and the childdata points assigned to each computer processing-thread using theplurality of computer processing-threads to generate a reconcileddataset, wherein reconciling the differences comprises each respectivecomputer processing-thread of the plurality of computerprocessing-threads adjusting values of the child data points that areassigned to the respective computer processing-thread such that anaggregate value of the adjusted versions of the child data points iswithin a tolerance range of a value of the parent data point; and adjustan operational setting of the machine based on the reconciled dataset.12. The node of claim 11, wherein reconciling the differences betweenthe parent data point and the child data points comprises adjusting atleast one value of at least one child data point to be closer to thevalue of the parent data point while maintaining the aggregate value ofthe adjusted versions of the child data points within the tolerancerange.
 13. The node of claim 11, wherein the plurality of computerprocessing-threads are configured to reconcile the differences betweenthe parent data point and the child data points in parallel to oneanother.
 14. The node of claim 11, wherein the distributed computingenvironment includes a cluster of nodes, each node in the cluster ofnodes has at least one respective processing device with multiplecomputer processing-threads, and each computer processing-thread amongthe multiple computer-processing threads is configured to solve arespective reconciliation problem related to a unique timestamp in theparent dataset by reconciling differences between (i) a particularparent data-point having the unique timestamp in the parent dataset and(ii) multiple child data-points having the unique timestamp in theplurality of child datasets.
 15. The node of claim 11, wherein thememory device further comprises instructions executable by theprocessing device for causing the processing device to: receive theparent timestamped data and the child timestamped data from a remotedatabase in response to a read command transmitted by the node, the readcommand being one of a plurality of read commands executed in parallelby the distributed computing environment; and write the reconcileddataset to the remote database for later retrieval.
 16. The node ofclaim 11, wherein the parent dataset and the plurality of child datasetsare predictive forecasts projecting how the machine will operate at afuture point in time.
 17. The node of claim 16, wherein the memorydevice further comprises instructions executable by the processingdevice for causing the processing device to: generate a predictiveforecast using reconciled dataset; and use the predictive forecast todetermine how the machine will operate at the future point in time. 18.The node of claim 17, wherein the memory device further comprisesinstructions executable by the processing device for causing theprocessing device to: monitor a machine using the predictive forecast toidentify an event associated with the machine; and adjust a parameter ofthe machine based on the event.
 19. The node of claim 17, wherein thememory device further comprises instructions executable by theprocessing device for causing the processing device to: detect ananomaly with a machine using the predictive forecast; and based ondetecting the anomaly, perform a task configured to reduce a likelihoodof the anomaly occurring.
 20. The node of claim 11, wherein the memorydevice further comprises instructions executable by the processingdevice for causing the processing device to: receive reconciled datasetsfrom other nodes in the distributed computing environment, eachreconciled dataset corresponding to different timestamps in the parentdataset; combine the reconciled datasets to form a combined reconcileddataset; and store the combined reconciled dataset in a database forlater retrieval.
 21. A non-transitory computer readable mediumcomprising program code executable by a processing device of a node in adistributed computing environment for causing the processing device to:receive parent timestamped data from a parent dataset and childtimestamped data from a plurality of child datasets that are children ofthe parent dataset in a hierarchical relationship, wherein the parenttimestamped data and the child timestamped data relate to an operationalcharacteristic of a machine; generate a plurality of computerprocessing-threads on a single processing device, each computerprocessing-thread of the plurality of computer processing-threads beingassigned to solve one or more respective reconciliation problems betweena parent data point that has a particular timestamp in the parenttimestamped data and child data points that also have the particulartimestamp in the child timestamp data; reconcile differences between theparent data point and the child data points assigned to each computerprocessing-thread using the plurality of computer processing-threads togenerate a reconciled dataset, wherein reconciling the differencescomprises each respective computer processing-thread of the plurality ofcomputer processing-threads adjusting values of the child data pointsthat are assigned to the respective computer processing-thread such thatan aggregate value of the adjusted versions of the child data points iswithin a tolerance range of a value of the parent data point; and adjustan operational setting of the machine based on the reconciled dataset.22. The non-transitory computer readable medium of claim 21, whereinreconciling the differences between the parent data point and the childdata points comprises adjusting at least one value of at least one childdata point to be closer to the value of the parent data point whilemaintaining the aggregate value of the adjusted versions of the childdata points within the tolerance range.
 23. The non-transitory computerreadable medium of claim 21, wherein the plurality of computerprocessing-threads are configured to reconcile the differences betweenthe parent data point and the child data points in parallel to oneanother.
 24. The non-transitory computer readable medium of claim 21,wherein the distributed computing environment includes a cluster ofnodes, each node in the cluster of nodes has at least one respectiveprocessing device with multiple computer processing-threads, and eachcomputer processing-thread among the multiple computer-processingthreads is configured to solve a respective reconciliation problemrelated to a unique timestamp in the parent dataset by reconcilingdifferences between (i) a particular parent data-point having the uniquetimestamp in the parent dataset and (ii) multiple child data-pointshaving the unique timestamp in the plurality of child datasets.
 25. Thenon-transitory computer readable medium of claim 21, further comprisinginstructions executable by the processing device for causing theprocessing device to: receive the parent timestamped data and the childtimestamped data from a remote database in response to a read commandtransmitted by the node, the read command being one of a plurality ofread commands executed in parallel by the distributed computingenvironment; and write the reconciled dataset to the remote database forlater retrieval.
 26. The non-transitory computer readable medium ofclaim 21, wherein the parent dataset and the plurality of child datasetsare predictive forecasts projecting how the machine will operate at afuture point in time.
 27. The non-transitory computer readable medium ofclaim 26, further comprising instructions executable by the processingdevice for causing the processing device to: generate a predictiveforecast using reconciled dataset; and use the predictive forecast todetermine how the machine will operate at the future point in time. 28.The non-transitory computer readable medium of claim 27, furthercomprising instructions executable by the processing device for causingthe processing device to: monitor a machine using the predictiveforecast to identify an event associated with the machine; and adjust aparameter of the machine based on the event.
 29. The non-transitorycomputer readable medium of claim 27, further comprising instructionsexecutable by the processing device for causing the processing deviceto: detect an anomaly with a machine using the predictive forecast; andbased on detecting the anomaly, perform a task configured to reduce alikelihood of the anomaly occurring.
 30. The non-transitory computerreadable medium of claim 21, further comprising instructions executableby the processing device for causing the processing device to: receivereconciled datasets from other nodes in the distributed computingenvironment, each reconciled dataset corresponding to differenttimestamps in the parent dataset; combine the reconciled datasets toform a combined reconciled dataset; and store the combined reconcileddataset in a database for later retrieval.